A comprehensive, project-driven roadmap to becoming an AI Engineer in 100 days
Listen up, aspiring AI Engineer!
I didn't spend countless hours researching, curating 150+ blog articles, creating 100 detailed daily checklists, and designing 7 production-grade projects for people who are "just browsing" or looking for "easy learning."
Before you scroll down, answer these questions HONESTLY:
- Can you dedicate 6-8 hours EVERY SINGLE DAY for 100 days?
- Can you code, build projects, and debug errors without giving up?
- Are you willing to share your learning publicly and hold yourself accountable?
- Can you handle failure, frustration, and the steep learning curve?
I'm serious. Leave. Go watch YouTube tutorials. Do easy Udemy courses. This isn't for you.
- ❌ No hand-holding - You'll get stuck. You'll debug for hours. That's the point.
- ❌ No participation trophies - Checking boxes without understanding = FAILURE.
- ❌ No excuses - "I was busy" = You weren't serious from the start.
- ✅ 100% commitment - Miss a day? START OVER. Yes, I'm that hardcore.
- ✅ Public accountability - Share EVERY DAY on social media or you're not doing this right.
- ✅ Real projects - Build production-grade systems, not toy examples.
- Code for 6-8 hours EVERY DAY for 100 consecutive days (NO EXCEPTIONS)
- Complete ALL coding tasks, exercises, and projects for each day
- Share my progress publicly EVERY SINGLE DAY using #100DaysOfAIEngineer
- Push my code to GitHub daily (no matter how ugly it is)
- Help others in the community when I can
- NOT give up when shit gets hard (because it WILL get hard)
- Build all 7 major projects from scratch
- Document my learning journey publicly
- Skip a single day
- Copy-paste code without understanding
- Make excuses
- Quit when it gets difficult
- Stay silent - I will engage with the community
- Half-ass any project or exercise
Here's what separates winners from wannabes:
WINNERS build in public, embrace the grind, show up every day, help others, and become job-ready AI Engineers.
WANNABES read day 1, get excited, quit by day 7, blame "lack of time," and never build anything real.
Post this on Twitter/LinkedIn/Instagram:
I'm committing to #100DaysOfAIEngineer starting TODAY.
100 days. No excuses. No skipping.
- 6-8 hours daily
- Public learning
- Real projects
- Building in public
Repo: [Your fork link]
Day 1 starts NOW. Who's with me? 🔥
#100DaysOfCode #MachineLearning #AI #LearningInPublic
Tag 3 friends who you think have the GUTS to do this with you.
After 100 days of this brutal, unforgiving, kick-your-ass curriculum, you'll:
- Build ML models from scratch in NumPy (not just using libraries like a script kiddie)
- Deploy production LLM applications with RAG, vector databases, and proper MLOps
- Implement neural networks, CNNs, transformers from first principles
- Have a portfolio with 7 production-grade projects that'll make recruiters drool
- Understand AI/ML at a level that 90% of "AI Engineers" don't
- Be able to pass technical interviews at top tech companies
- Actually DESERVE to call yourself an AI Engineer
- Entry-level AI Engineer salary: $80k-120k
- Skills that companies are desperately hiring for RIGHT NOW
- Portfolio that stands out from the "I completed a Coursera course" crowd
- Confidence to build ANY AI system from scratch
This curriculum was designed by someone who gives a shit about your SUCCESS, not your COMFORT.
If you're looking for easy: This isn't it. Leave now. If you're looking for quick: This isn't it. Leave now. If you're not willing to suffer a little: This DEFINITELY isn't it. LEAVE NOW.
BUT...
If you're ready to transform yourself. If you're hungry to actually BECOME an AI Engineer, not just "learn about AI." If you're willing to embrace the suck for 100 days to change your life.
People who started: [TBD] People who quit in week 1: [TBD] People who made it to day 30: [TBD] People who finished all 100 days: [TBD] People who got hired as AI Engineers: [TBD]
Will you be in the "finished" column or the "quit" column?
Click here to start: Day 1 Checklist
Fork this repo. Share your commitment. Begin.
The world doesn't need more people who "tried" AI. It needs people who BECAME AI Engineers.
NOW GET THE FUCK TO WORK. 💪🔥
P.S. - If this notice offended you, good. You probably weren't ready anyway. If this fired you up, PERFECT. You're exactly who this is for. See you at Day 100. 🎯
You CAN'T do this alone. And you don't have to.
Join NOW: https://discord.gg/9eFXYntYa8
Why Discord is MANDATORY:
- ✅ Daily accountability - Post your progress EVERY DAY
- ✅ Get unstuck FAST - Community answers in minutes, not hours
- ✅ Code reviews - Get feedback from other learners and mentors
- ✅ Stay motivated - See others crushing it, get inspired
- ✅ Network - Connect with future AI Engineers and potential employers
- ✅ Peer pressure (the good kind) - Your streak is PUBLIC
📢 Key Channels:
- #100daysofaiengineer - Daily check-ins and quick updates
- 100daysofaiengineer forum - Project showcases, code reviews, detailed discussions
📅 What You'll Post Daily:
Day X/100 ✅
Topic: [What you learned]
Code: [GitHub link]
Progress: [What you built]
#100DaysOfAIEngineer
All platforms: @CODERCOPS
- 🐦 Twitter/X: https://twitter.com/CODERCOPS - Daily AI tips, community wins
- 💼 LinkedIn: https://linkedin.com/company/CODERCOPS - Professional updates, job posts
- 📸 Instagram: https://instagram.com/CODERCOPS - Visual progress, motivation
- 🎥 YouTube: https://youtube.com/@CODERCOPS - Tutorials, project walkthroughs
- 💻 GitHub: https://github.com/CODERCOPS - Open source projects, resources
Follow all platforms. Engage. Tag @CODERCOPS in your posts. Build in public.
- Join Discord: https://discord.gg/9eFXYntYa8
- Introduce yourself in #introductions (if channel exists)
- Follow @CODERCOPS on all platforms
- Post your commitment on social media (template above)
- Start Day 1 (link below)
Related Docs:
- 📘 COMMUNITY.md - How to engage in Discord
- 📜 COMMUNITY_GUIDELINES.md - Server rules
- 📊 ACCOUNTABILITY.md - Daily posting requirements
This is a structured 100-day program designed to transform you from a Python developer into a skilled AI Engineer. The curriculum is project-focused, hands-on, and covers the modern AI stack used in production environments.
🎯 Daily Checklists Directory - Track your progress day by day!
Each day includes:
- ✅ Checklist format - Mark tasks as you complete them
- ✅ Learning objectives - Clear daily goals
- ✅ Coding tasks - Specific implementations
- ✅ Social media templates - Ready-to-post updates for Twitter, LinkedIn, Instagram
- ✅ Reflection prompts - Document your journey
Why share on social media?
- 📢 Accountability through public commitment
- 🤝 Connect with other learners (#100DaysOfAIEngineer)
- 💼 Build your professional brand
- 📊 Track your progress publicly
- 🎯 Stay motivated through community support
👉 Start with Day 1 Checklist
- 7 Major Real-World Projects
- 15+ Mini Projects
- Production-ready ML/AI applications
- Full MLOps pipeline
- LLM-powered applications
- ✅ Python programming knowledge
- ✅ Basic understanding of programming concepts
- ✅ Willingness to code daily
- ✅ 6-8 hours daily commitment
Phase 1: Foundations & Classical ML (Days 1-15)
Phase 2: Deep Learning Fundamentals (Days 16-30)
Phase 3: Computer Vision (Days 31-45)
Phase 4: Natural Language Processing (Days 46-60)
Phase 5: LLMs & Modern NLP (Days 61-75)
Phase 6: MLOps & Production (Days 76-85)
Phase 7: Capstone & Advanced Topics (Days 86-100)
Goal: Master data manipulation, classical ML algorithms, and build your first ML pipeline
Day 1-2: NumPy Mastery
- Array operations, broadcasting, vectorization
- Linear algebra operations (dot products, matrix multiplication)
- Random sampling and statistical operations
- Exercise: Implement matrix operations from scratch
- Mini Project: Build a simple image filter using NumPy
Day 3-4: Pandas for Data Manipulation
- DataFrames, Series, indexing
- Data cleaning, handling missing values
- Groupby, pivot tables, merging
- Exercise: Analyze a real dataset (Kaggle dataset)
- Mini Project: COVID-19 data analysis dashboard
Day 5-6: Data Visualization
- Matplotlib, Seaborn, Plotly
- Statistical plots, distributions
- Interactive visualizations
- Mini Project: Exploratory Data Analysis (EDA) on Titanic dataset
Day 7: Mathematics for ML
- Linear algebra review (vectors, matrices, eigenvalues)
- Calculus basics (derivatives, gradients)
- Probability and statistics fundamentals
- Exercise: Implement gradient descent from scratch
Day 8-9: Supervised Learning - Regression
- Linear regression (theory + implementation from scratch)
- Polynomial regression, regularization (Ridge, Lasso)
- Gradient descent optimization
- Exercise: Predict house prices using linear regression
- Code: Implement linear regression without sklearn
Day 10-11: Supervised Learning - Classification
- Logistic regression
- Decision trees and Random Forests
- Support Vector Machines (SVM)
- Exercise: Binary and multi-class classification problems
- Mini Project: Spam email classifier
Day 12-13: Unsupervised Learning
- K-Means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
- DBSCAN
- Mini Project: Customer segmentation using clustering
Day 14: Model Evaluation & Feature Engineering
- Cross-validation, train-test split
- Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
- Feature scaling, encoding categorical variables
- Handling imbalanced datasets
- Exercise: Compare multiple models on a dataset
Day 15: 🎯 PROJECT 1 - End-to-End ML Pipeline
- Build: Complete ML pipeline for a real-world problem
- Data collection and cleaning
- Feature engineering and selection
- Model training, evaluation, hyperparameter tuning
- Deliverable: Jupyter notebook with full pipeline
- Suggested: Predict customer churn or credit card fraud detection
Goal: Understand neural networks and implement deep learning models
Day 16-17: Neural Network Fundamentals
- Perceptron, activation functions
- Forward propagation
- Loss functions (MSE, Cross-entropy)
- Exercise: Implement a perceptron from scratch
Day 18-19: Backpropagation
- Chain rule and backpropagation algorithm
- Gradient descent variants (SGD, Momentum, Adam)
- Exercise: Implement backpropagation from scratch
- Code: Build a 2-layer neural network without frameworks
Day 20-21: Introduction to PyTorch
- Tensors, autograd, computational graphs
- Building models with nn.Module
- Training loops, optimizers
- Exercise: Reimplement Day 19 network in PyTorch
- Mini Project: MNIST digit classification
Day 22: Regularization & Optimization
- Dropout, Batch Normalization, L1/L2 regularization
- Learning rate scheduling
- Early stopping
- Exercise: Prevent overfitting on a deep network
Day 23-24: Convolutional Neural Networks (CNNs) - Part 1
- Convolution operation, pooling
- CNN architectures (LeNet, AlexNet)
- Exercise: Visualize filters and feature maps
- Code: Build a simple CNN for image classification
Day 25-26: CNNs - Part 2 & Transfer Learning
- Modern architectures (VGG, ResNet, EfficientNet)
- Transfer learning and fine-tuning
- Data augmentation
- Mini Project: Fine-tune ResNet on a custom dataset
Day 27-28: Recurrent Neural Networks (RNNs)
- Sequence modeling, RNN architecture
- LSTM and GRU
- Vanishing gradient problem
- Exercise: Time series prediction with LSTM
- Mini Project: Stock price prediction
Day 29: Handling Real-World Data
- Data preprocessing pipelines
- DataLoaders and Dataset classes in PyTorch
- Handling large datasets
- Exercise: Build efficient data pipelines
Day 30: 🎯 PROJECT 2 - Image Classification System
- Build: End-to-end image classifier with web interface
- Custom dataset creation and preprocessing
- Train CNN from scratch + transfer learning comparison
- Model evaluation and visualization
- Deliverable: Streamlit/Gradio app for image classification
- Suggested: Plant disease classifier or animal species identifier
Goal: Master computer vision techniques and build production-ready CV applications
Day 31-32: Object Detection - Part 1
- R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN)
- YOLO (You Only Look Once) architecture
- Exercise: Understand anchor boxes and IoU
- Code: Implement basic object detection with YOLOv5
Day 33-34: Object Detection - Part 2
- YOLOv8, DETR (Detection Transformer)
- Non-maximum suppression (NMS)
- mAP (mean Average Precision) metric
- Mini Project: Real-time object detection with webcam
Day 35-36: Semantic Segmentation
- U-Net architecture
- Mask R-CNN
- DeepLab
- Exercise: Image segmentation on medical images
- Mini Project: Background removal tool
Day 37: Instance Segmentation & Pose Estimation
- Instance segmentation with Mask R-CNN
- Human pose estimation (OpenPose, MediaPipe)
- Mini Project: Pose-based fitness rep counter
Day 38-39: Generative Models - Part 1
- Autoencoders and Variational Autoencoders (VAE)
- Image denoising and reconstruction
- Exercise: Build an autoencoder for MNIST
- Mini Project: Image denoising application
Day 40-41: Generative Models - Part 2 (GANs)
- Generative Adversarial Networks (GANs)
- DCGAN, StyleGAN basics
- GAN training challenges
- Mini Project: Generate synthetic images
Day 42-43: Modern CV Techniques
- Vision Transformers (ViT)
- CLIP (Contrastive Language-Image Pre-training)
- Image captioning
- Exercise: Use CLIP for zero-shot classification
- Mini Project: Image search engine using CLIP
Day 44: Model Optimization for CV
- Model quantization and pruning
- ONNX export
- TensorRT optimization
- Exercise: Optimize a model for edge deployment
Day 45: 🎯 PROJECT 3 - Smart Surveillance System
- Build: Real-time object detection and tracking system
- Multiple object tracking (MOT)
- Alert system for specific objects/behaviors
- Performance optimization for real-time processing
- Deliverable: Real-time video analysis application
- Suggested: People counter, vehicle detection, or safety monitoring
Goal: Master NLP fundamentals and build text-based AI applications
Day 46-47: Text Preprocessing & Feature Engineering
- Tokenization, stemming, lemmatization
- Stop words removal, text cleaning
- Bag of Words, TF-IDF
- Exercise: Build a text preprocessing pipeline
- Mini Project: Document similarity finder
Day 48-49: Word Embeddings
- Word2Vec (CBOW, Skip-gram)
- GloVe, FastText
- Embedding visualization (t-SNE)
- Exercise: Train custom word embeddings
- Mini Project: Word analogy solver (king - man + woman = queen)
Day 50-51: Text Classification
- Sentiment analysis
- Naive Bayes, Logistic Regression for text
- Deep learning for text (CNN, LSTM)
- Mini Project: Movie review sentiment classifier
- Exercise: Multi-label text classification
Day 52: Named Entity Recognition (NER)
- NER with spaCy
- Custom NER models
- Mini Project: Extract entities from news articles
Day 53-54: Sequence-to-Sequence Models
- Encoder-decoder architecture
- Seq2Seq with attention
- Exercise: Build a simple translation model
- Mini Project: Text summarization tool
Day 55-56: Attention Mechanism & Transformers
- Self-attention, multi-head attention
- Transformer architecture deep dive
- Positional encoding
- Exercise: Implement self-attention from scratch
- Code: Build a mini transformer
Day 57-58: Pre-trained Language Models
- BERT, RoBERTa, DistilBERT
- Fine-tuning BERT for classification
- Feature extraction with BERT
- Mini Project: Question-answering system with BERT
Day 59: Advanced NLP Tasks
- Text generation
- Zero-shot classification
- Few-shot learning with GPT
- Exercise: Experiment with Hugging Face Transformers
Day 60: 🎯 PROJECT 4 - NLP Multi-Task Application
- Build: Comprehensive text analysis tool
- Sentiment analysis
- Named entity recognition
- Text summarization
- Topic modeling
- Deliverable: Web API (FastAPI) with multiple NLP endpoints
- Suggested: News article analyzer or social media insights tool
Goal: Master LLMs, RAG systems, and build production LLM applications
Day 61-62: Understanding LLMs
- GPT architecture deep dive
- Tokenization (BPE, WordPiece)
- LLM training process (pre-training, fine-tuning)
- Exercise: Explore GPT-2/GPT-3 API
- Mini Project: Text completion app
Day 63-64: Prompt Engineering
- Zero-shot, few-shot, chain-of-thought prompting
- Prompt templates and optimization
- In-context learning
- Exercise: Build a prompt library for common tasks
- Mini Project: AI assistant with optimized prompts
Day 65-66: Fine-Tuning LLMs
- Full fine-tuning vs LoRA vs QLoRA
- Parameter-efficient fine-tuning (PEFT)
- Instruction tuning
- Exercise: Fine-tune a small LLM (Flan-T5, GPT-2)
- Mini Project: Domain-specific chatbot
Day 67-68: LangChain Framework
- LangChain basics (chains, agents, memory)
- Prompt templates and output parsers
- LLM chains and sequential chains
- Exercise: Build complex chains
- Mini Project: Research assistant with LangChain
Day 69-70: Vector Databases & Embeddings
- Embedding models (OpenAI, Sentence-Transformers)
- Vector databases (Pinecone, Weaviate, ChromaDB, FAISS)
- Similarity search
- Exercise: Build a semantic search engine
- Mini Project: Document similarity search
Day 71-72: Retrieval-Augmented Generation (RAG)
- RAG architecture and workflow
- Document loading, chunking strategies
- Retrieval optimization
- Exercise: Build a basic RAG system
- Mini Project: "Chat with your PDF" application
Day 73: Advanced RAG Techniques
- Hybrid search (keyword + semantic)
- Re-ranking and MMR (Maximal Marginal Relevance)
- Metadata filtering
- Exercise: Improve RAG system accuracy
Day 74: LLM Evaluation & Safety
- Evaluation metrics for LLMs
- Guardrails and content filtering
- Handling hallucinations
- Cost optimization
- Exercise: Benchmark different LLM approaches
Day 75: 🎯 PROJECT 5 - Production RAG Application
- Build: ChatGPT-like application with custom knowledge base
- Multi-document RAG system
- Conversation memory and context management
- Source attribution
- Deliverable: Full-stack application (FastAPI + React/Streamlit)
- Suggested: Company knowledge base chatbot or legal document Q&A
Goal: Learn to deploy, monitor, and maintain ML models in production
Day 76-77: Model Serving & APIs
- FastAPI for ML models
- REST API design for ML
- Request/response handling
- Model versioning
- Exercise: Create API endpoints for multiple models
- Mini Project: Model serving API with FastAPI
Day 78: Docker for ML
- Containerization basics
- Docker for ML applications
- Multi-stage builds
- Exercise: Dockerize ML application
- Mini Project: Docker Compose setup for ML app + database
Day 79: Model Optimization
- Model quantization (int8, fp16)
- Knowledge distillation
- ONNX Runtime
- Exercise: Optimize model for inference
- Mini Project: Compare inference speeds
Day 80-81: ML Experiment Tracking
- MLflow for experiment tracking
- Weights & Biases (W&B)
- Model registry
- Exercise: Track experiments for a model
- Mini Project: Complete ML experiment pipeline
Day 82: Model Monitoring & Observability
- Model drift detection
- Data drift monitoring
- Logging and alerting
- Exercise: Set up monitoring dashboard
- Tools: Evidently AI, WhyLabs
Day 83: CI/CD for ML
- GitHub Actions for ML
- Automated testing for ML code
- Continuous training
- Exercise: Build CI/CD pipeline
- Mini Project: Automated model retraining pipeline
Day 84: Cloud Deployment
- AWS SageMaker basics (or GCP Vertex AI)
- Serverless deployment (AWS Lambda)
- Scaling strategies
- Exercise: Deploy model to cloud
Day 85: 🎯 PROJECT 6 - Production ML System
- Build: End-to-end production ML system
- Model training pipeline
- Automated deployment
- Monitoring and alerting
- CI/CD integration
- Deliverable: Fully deployed, monitored ML application
- Suggested: Real-time recommendation system or fraud detection
Goal: Build a comprehensive AI project and explore cutting-edge topics
Day 86-87: Multi-Modal AI
- Vision-language models (CLIP, BLIP)
- Audio processing (Whisper)
- Multi-modal applications
- Mini Project: Image captioning or visual question answering
Day 88-89: AI Agents & LangGraph
- Autonomous agents
- ReAct framework
- Tool use with LLMs
- Mini Project: AI agent that can browse and use tools
Day 90-91: Reinforcement Learning Basics
- MDP, Q-learning basics
- Policy gradients
- RL for practical applications
- Exercise: Train an agent in a simple environment
- Mini Project: Game-playing AI
Day 92-93: Advanced Generative AI
- Stable Diffusion and image generation
- ControlNet, LoRA for Stable Diffusion
- Text-to-image applications
- Mini Project: AI art generator
Day 94-100: 🎯 CAPSTONE PROJECT - Full-Stack AI Application
Build a comprehensive, production-ready AI application that combines multiple concepts:
Project Ideas:
-
AI-Powered Content Platform
- Text generation, image generation
- RAG-based Q&A
- Content moderation
- User analytics
-
Intelligent Personal Assistant
- Voice input (Whisper)
- Multi-turn conversations with memory
- Tool use (calendar, email, web search)
- Task automation
-
AI-Powered Healthcare Assistant
- Medical image analysis
- Symptom checker with RAG
- Health record summarization
- Privacy-preserving design
-
Smart Education Platform
- Personalized learning paths
- Auto-grading with explanations
- Interactive tutoring chatbot
- Progress tracking
Requirements:
- Frontend (React/Vue or Streamlit)
- Backend API (FastAPI)
- Multiple AI models (CV + NLP + LLM)
- Database integration
- Authentication & authorization
- Docker deployment
- Monitoring and logging
- Documentation
Deliverables:
- Complete codebase on GitHub
- Deployed application
- Technical documentation
- Demo video
- Blog post explaining architecture
- Languages: Python
- DL Frameworks: PyTorch, TensorFlow/Keras
- ML Libraries: scikit-learn, XGBoost, LightGBM
- NLP: Hugging Face Transformers, spaCy, NLTK
- LLM: OpenAI API, LangChain, LlamaIndex
- Vector DBs: ChromaDB, FAISS, Pinecone
- Computer Vision: OpenCV, torchvision, timm
- Data: NumPy, Pandas, Polars
- Visualization: Matplotlib, Seaborn, Plotly
- MLOps: MLflow, Weights & Biases, DVC
- Deployment: FastAPI, Docker, AWS/GCP
- Version Control: Git, GitHub
# Create conda environment
conda create -n ai-engineer python=3.10
conda activate ai-engineer
# Install core packages
pip install torch torchvision torchaudio
pip install transformers datasets
pip install langchain openai chromadb
pip install fastapi uvicorn
pip install mlflow wandb
pip install streamlit gradio
pip install scikit-learn pandas numpy matplotlib seabornCreate a daily log:
## Day X: [Topic]
### What I Learned
- Key concept 1
- Key concept 2
### Code Implemented
- [Link to code/notebook]
### Challenges Faced
- Challenge and how I solved it
### Resources Used
- Tutorial/article links
### Tomorrow's Goal
- What I plan to learn next🔥 BLOG_ARTICLES.md - 150+ Curated Blog Posts
We've researched and compiled 150+ high-quality blog articles from trusted sources for every topic in the curriculum:
- ✅ Organized by Phase: Articles matched to each day's learning
- ✅ 2024-2025 Content: Latest tutorials and best practices
- ✅ Code Examples: All include practical implementations
- ✅ Verified Quality: Hand-picked from top platforms (Medium, Towards Data Science, official docs)
Topics Include: NumPy, Pandas, ML algorithms, PyTorch, CNNs, YOLO, NLP, BERT, Transformers, LLMs, Fine-tuning, RAG, LangChain, Vector Databases, MLOps, Docker, and more!
👉 See BLOG_ARTICLES.md for the complete collection
Also check RESOURCES.md for comprehensive books, courses, tools, and platforms.
- Fast.ai - Practical Deep Learning
- Stanford CS229 - Machine Learning
- Stanford CS224N - NLP with Deep Learning
- DeepLearning.AI - Deep Learning Specialization
- Hugging Face Course - NLP with Transformers
- "Hands-On Machine Learning" by Aurélien Géron
- "Deep Learning" by Ian Goodfellow
- "Natural Language Processing with Transformers" by Lewis Tunstall
- "Designing Machine Learning Systems" by Chip Huyen
- Kaggle - Competitions and datasets
- Papers with Code - Latest research
- Hugging Face - Models and datasets
- GitHub - Open source projects
- Andrej Karpathy
- StatQuest
- 3Blue1Brown (Math)
- Yannic Kilcher (Paper reviews)
- Code Every Day - Even 30 minutes counts
- Build Projects - Theory without practice is useless
- Read Research Papers - Stay updated with latest techniques
- Join Communities - Reddit (r/MachineLearning), Discord servers
- Document Your Journey - Blog, GitHub, LinkedIn posts
- Don't Just Tutorial Hell - Build original projects
- Understand, Don't Memorize - Focus on concepts, not code
- Debug and Experiment - Break things and fix them
- Review Regularly - Revisit concepts weekly
- Stay Consistent - 100 days straight is better than random practice
By Day 100, you should be able to:
- Build and deploy ML models end-to-end
- Implement neural networks from scratch
- Fine-tune and deploy LLMs
- Create RAG applications
- Build computer vision applications
- Design and implement MLOps pipelines
- Read and implement research papers
- Contribute to open source ML projects
- Pass AI Engineer technical interviews
100DaysOfAIEngineer/
│
├── 📄 README.md # Main curriculum & overview
│
├── 📚 Learning & Resources:
│ ├── RESOURCES.md # Curated learning resources
│ ├── PROJECT_GUIDE.md # Project specifications
│ ├── BLOG_ARTICLES.md # 150+ curated blog posts
│ └── FAQ.md # Frequently asked questions
│
├── 🤝 Community & Accountability:
│ ├── COMMUNITY.md # CODERCOPS Discord integration
│ ├── COMMUNITY_GUIDELINES.md # Community rules
│ ├── ACCOUNTABILITY.md # Daily tracking system
│ ├── PEER_REVIEW_GUIDE.md # Code review guidelines
│ └── HALL_OF_FAME.md # Graduate recognition
│
├── 🎯 Quality & Standards:
│ ├── QUALITY_STANDARDS.md # Completion criteria
│ ├── ANTI_PATTERNS.md # Common mistakes to avoid
│ └── FAILURE_RECOVERY.md # Restart protocols
│
├── 💼 Career Development:
│ └── JOB_HUNTING_PLAYBOOK.md # Job search strategies
│
├── 📁 daily_checklists/ # ⭐ CORE CURRICULUM
│ ├── day01/ # NumPy Basics
│ │ ├── README.md # Daily guide with resources
│ │ ├── code/ # Your code here
│ │ ├── notebooks/ # Jupyter notebooks
│ │ └── notes.md # Personal notes
│ ├── day02/ # Advanced NumPy
│ ├── day03/ # Pandas Fundamentals
│ │ └── ...
│ └── day100/ # 🎉 Celebration & Reflection
│
└── 📁 weekly_reviews/ # Weekly reflection & planning
├── week01/
├── week02/
│ └── ...
└── week14/
How to use this repository:
- Start here: Read this README completely
- Join community: COMMUNITY.md - Discord is REQUIRED
- Begin Day 1: daily_checklists/day01/
- Track progress: Update your daily README, post in Discord
- Review weekly: Complete weekly reflections in weekly_reviews/
- Build projects: Push your code to each day's directory
- Stay accountable: Daily Discord posts, 3x/week social media
Found an error or want to improve the curriculum? Feel free to:
- Open an issue
- Submit a pull request
- Share your progress and projects
MIT License - Feel free to use and adapt this roadmap for your learning journey!
Start with Day 1 and commit to the journey. Remember: Consistency beats intensity.
Your AI Engineering journey starts now! 🎉
Created with ❤️ for aspiring AI Engineers
Last Updated: 2025